Description

Details

This is a package for estimating nonlinear structural equation mixture
models using an expectation-maximization (EM) algorithm. Four different
approaches are implemented. Firstly, the Latent Moderated Structural
Equations (LMS) approach (Klein & Moosbrugger, 2000) and the Quasi-Maximum
Likelihood (QML) approach (Klein & Muthen, 2007), which allow for two-way
interaction and quadratic terms in the structural model. Due to the
nonlinearity, the latent criterion variables cannot be assumed to be
normally distributed. Therefore, the latent criterins's distribution is
approximated with a mixture of normal distributions in LMS. Secondly, the
Structural Equation finite Mixture Model (STEMM or SEMM) approach (Jedidi,
Jagpal & DeSarbo, 1997), which uses mixtures to model latent classes. In
this way it can deal with heterogeneity in the sample or nonlinearity and
nonnormality of the latent variables and their indicators. And thirdly, a
combination of these two approaches, the Nonlinear Structural Equation
Mixture Model (NSEMM) approach (Kelava, Nagengast & Brandt, 2014). Here,
interaction and quadratic terms as well as latent classes can be modeled.

The models can be specified with specify_sem. Depending
on the specification of interaction and the number of latent classes
(num.classes) the returned object will be of class
singleClass, semm, or nsemm. Each of these can be
estimated using em and models of type singleClass
can additionally be fitted with the function qml.